t tests state the meaning of μ first – solutions on page 629 (p value = .0569) p 345 9.74 a -e Use the TI-84 t test and the p value decision rule. Z test for the proportion State the meaning of P...

t tests   state the meaning of μ first – solutions on page 629 (p value = .0569)

p 345           9.74 a -e      Use the TI-84 t test and the p value decision rule.

Z test for the proportion   State the meaning of P first - solutions on page 629

p 340           9.56 b   Use the TI-84 one prop Z test and the p value decision rule,                          plus determine a (1-α)% CI for P        CI for P: (.5608, .6392)

                  Does the CI support the decision?

2 sample t test   State µ₁
and µ₂
first and use the TI-84 2 sample t test and interval

Determine at the 5% level if there is evidence from the sample data that the mean battery life is significantly higher for the tablet with WiFi than the tablet with 4G.  Plus calculate and
completely
interpret a (1-α)% CI for µ_WiFi
- µ_4G.  Does the interval support the decision?  If yes, then how many more hours does the battery last for the WiFi tablet?  The pooled variance t_STAT
= 3.3689 and the p value = .001822; the CI for µ_WiFi
- µ_4G
is between (1.7477, 7.6047) lifetime hours.

Chi Square Tests   State the meaning of the P’^s
and the value of the ’^s
for the ‘difference in proportion Χ²
test’; and for ‘Χ²
test for independence’ state the meaning of the two categories; use the p value decision rule - solutions on page 663

p 443           11.32 b   hint: there are 5 P’s

p 440           11.24            the p value = 0.00 <>

Simple Regression Analysis   Solutions will be here after the due date…

We want to develop a regression model to predict the assessed value of houses based on the heating area of houses in square feet.  A sample of 15 single-family houses in the Kalamazoo area is selected.  The assessed value in dollars and the heating area of the houses in square feet are recorded and stored in the data set
House 3
(in the class Minitab Files folder).  We are not using the Age in years column.

1. Interpret the meaning of the Y intercept; b₀
   and the slope b₁
   in the model.

1. Predict the assessed value for a house whose heating area is 1,750 square feet.

1. Determine the coefficient of determination, r², and interpret its meaning.

1. At the 0.05 level of significance, is there evidence of a significant linear relationship between assessed value and heating area?

1. e) Determine the S.D. of ŷ. What units is it in?

1. f) Construct a 95% CI for the slope B₁
   

1. g) Determine a 95% interval estimate for the assessed value of a home with 1750 sq. ft. of heating area.

1. h) What is the strength of the linear relationship between assessed value and heating area?

Regression Analysis: Assessed Value versus Heating Area

Model Summary

Coefficients

Regression Equation

 Prediction